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11 pages, 6 figures, supplementary information https://doi.org/10.1038/s41612-021-00188-5.-- Data availability; The data generated and analyzed during the current study are available from the corresponding author YRR on reasonable request ; Atlantic multidecadal variability (AMV) has been linked to the observed slowdown of global warming over 1998–2012 through its impact on the tropical Pacific. Given the global importance of tropical Pacific variability, better understanding this Atlantic–Pacific teleconnection is key for improving climate predictions, but the robustness and strength of this link are uncertain. Analyzing a multi-model set of sensitivity experiments, we find that models differ by a factor of 10 in simulating the amplitude of the Equatorial Pacific cooling response to observed AMV warming. The inter-model spread is mainly driven by different amounts of moist static energy injection from the tropical Atlantic surface into the upper troposphere. We reduce this inter-model uncertainty by analytically correcting models for their mean precipitation biases and we quantify that, following an observed 0.26 °C AMV warming, the equatorial Pacific cools by 0.11 °C with an inter-model standard deviation of 0.03 °C ; Y.R.-R. was founded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the Marie Skłodowska-Curie grant INADEC (Grant agreement 800154). E.M.-C. acknowledges funding from the European Commission's Horizon 2020 projects PRIMAVERA (Grant Agreement 641727). X.L. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433. A.B. and D.N. acknowledge funding from the European Commission's Horizon 2020 project EUCP (Grant agreement 776613). F.C. and G.D. were supported by the US National Science Foundation (NSF) under the Collaborative Research EaSM2 Grant OCE-1243015 to NCAR and by the US National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under the Climate Variability and Predictability Program Grant NA13OAR4310138. NCAR is a major facility sponsored by the US NSF under Cooperative Agreement 1852977. [.] R.E., N.D., L.H., and D.S. were supported by the Met Office Hadley Center 522 Climate Program funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP 523 project (GA 776613). J.L.-P. was funded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the PRIMAVERA project (Grant Agreement 641727). J.R. and D.H. were funded by NERC via NCAS and the ACSIS project (NE/N018001/1), and JR was also funded by the NERC SMURPHS project (NE/N006054/1). M.M.-R. was funded by the European Union's Horizon 2020 Research and Innovation Program in the framework of the Marie Skłodowska-Curie grant FESTIVAL (Grant agreement 797236). E.T. has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Skłodowska-Curie grant agreement No. 748750 (SPFireSD project) ; With the funding support of the 'Severo Ochoa Centre of Excellence' accreditation (CEX2019-000928-S), of the Spanish Research Agency (AEI) ; Peer reviewed

Quantifying signals and uncertainties in climate models is essential for the detection, attribution, prediction and projection of climate change1,2,3. Although inter-model agreement is high for large-scale temperature signals, dynamical changes in atmospheric circulation are very uncertain4. This leads to low confidence in regional projections, especially for precipitation, over the coming decades5,6. The chaotic nature of the climate system7,8,9 may also mean that signal uncertainties are largely irreducible. However, climate projections are difficult to verify until further observations become available. Here we assess retrospective climate model predictions of the past six decades and show that decadal variations in North Atlantic winter climate are highly predictable, despite a lack of agreement between individual model simulations and the poor predictive ability of raw model outputs. Crucially, current models underestimate the predictable signal (the predictable fraction of the total variability) of the North Atlantic Oscillation (the leading mode of variability in North Atlantic atmospheric circulation) by an order of magnitude. Consequently, compared to perfect models, 100 times as many ensemble members are needed in current models to extract this signal, and its effects on the climate are underestimated relative to other factors. To address these limitations, we implement a two-stage post-processing technique. We first adjust the variance of the ensemble-mean North Atlantic Oscillation forecast to match the observed variance of the predictable signal. We then select and use only the ensemble members with a North Atlantic Oscillation sufficiently close to the variance-adjusted ensemble-mean forecast North Atlantic Oscillation. This approach greatly improves decadal predictions of winter climate for Europe and eastern North America. Predictions of Atlantic multidecadal variability are also improved, suggesting that the North Atlantic Oscillation is not driven solely by Atlantic multidecadal variability. Our results highlight the need to understand why the signal-to-noise ratio is too small in current climate models10, and the extent to which correcting this model error would reduce uncertainties in regional climate change projections on timescales beyond a decade. ; DMS, AAS, NJD, LH and RE were supported by the Met Office Hadley Centre Climate Programme funded by BEIS and Defra and by the European Commission Horizon 2020 EUCP project (GA 776613). FJDR, LPC, SW and RB also acknowledge the support from the EUCP project (GA 776613) and from the Ministerio de Econom´ıa y Competitividad (MINECO) as part of the CLINSA project (Grant No. CGL2017-85791-R). SW received funding from the innovation programme under the Marie Sk´lodowska-Curie grant agreement H2020-MSCA-COFUND-2016-754433 and PO from the Ramon y Cajal senior tenure programme of MINECO. The EC-Earth simulations were performed on Marenostrum 4 (hosted by the Barcelona Supercomputing Center, Spain) using Auto-Submit through computing hours provided by PRACE.WAM, HP, KMand KP were supported by the German FederalMinistry for Education and Research (BMBF) project MiKlip (grant 01LP1519A). NK, IB, FC and YW were supported by the Norwegian Research Council projects SFE (grant 270733) the Nordic Center of excellent ARCPATH (grant 76654) and the Trond Mohn Foundation, under the project number : BFS2018TMT01 and received grants for computer time from the Norwegian Program for supercomputing (NOTUR2, NN9039K) and storage grants (NORSTORE, NS9039K). JM, LFB and DS are supported by Blue-Action (European Union Horizon 2020 research and innovation program, Grant Number: 727852) and EUCP (European Union Horizon 2020 research and innovation programme under grant agreement no 776613) projects. The National Center for Atmospheric Research (NCAR) is a major facility sponsored by the US National Science Foundation (NSF) under Cooperative Agreement No. 1852977. NCAR contribution was partially supported by the National Oceanic and Atmospheric Administration (NOAA) Climate Program Office under Climate Variability and Predictability Program Grant NA13OAR4310138 and by the US NSF Collaborative Research EaSM2 Grant OCE-1243015. ; Peer Reviewed ; Postprint (author's final draft)

36 pages, 12 figures, 1 table ; he tropical Atlantic is home to multiple coupled climate variations covering a wide range of timescales and impacting societally relevant phenomena such as continental rainfall, Atlantic hurricane activity, oceanic biological productivity, and atmospheric circulation in the equatorial Pacific. The tropical Atlantic also connects the southern and northern branches of the Atlantic meridional overturning circulation and receives freshwater input from some of the world's largest rivers. To address these diverse, unique, and interconnected research challenges, a rich network of ocean observations has developed, building on the backbone of the Prediction and Research Moored Array in the Tropical Atlantic (PIRATA). This network has evolved naturally over time and out of necessity in order to address the most important outstanding scientific questions and to improve predictions of tropical Atlantic severe weather and global climate variability and change. The tropical Atlantic observing system is motivated by goals to understand and better predict phenomena such as tropical Atlantic interannual to decadal variability and climate change; multidecadal variability and its links to the meridional overturning circulation; air-sea fluxes of CO2 and their implications for the fate of anthropogenic CO2; the Amazon River plume and its interactions with biogeochemistry, vertical mixing, and hurricanes; the highly productive eastern boundary and equatorial upwelling systems; and oceanic oxygen minimum zones, their impacts on biogeochemical cycles and marine ecosystems, and their feedbacks to climate. Past success of the tropical Atlantic observing system is the result of an international commitment to sustained observations and scientific cooperation, a willingness to evolve with changing research and monitoring needs, and a desire to share data openly with the scientific community and operational centers. The observing system must continue to evolve in order to meet an expanding set of research priorities and operational challenges. This paper discusses the tropical Atlantic observing system, including emerging scientific questions that demand sustained ocean observations, the potential for further integration of the observing system, and the requirements for sustaining and enhancing the tropical Atlantic observing system ; MM-R received funding from the MORDICUS grant under contract ANR-13-SENV-0002-01 and the MSCA-IF-EF-ST FESTIVAL (H2020-EU project 797236). GF, MG, RLu, RP, RW, and CS were supported by NOAA/OAR through base funds to AOML and the Ocean Observing and Monitoring Division (OOMD; fund reference 100007298). This is NOAA/PMEL contribution #4918. PB, MDe, JH, RH, and JL are grateful for continuing support from the GEOMAR Helmholtz Centre for Ocean Research Kiel. German participation is further supported by different programs funded by the Deutsche Forschungsgemeinschaft, the Deutsche Bundesministerium für Bildung und Forschung (BMBF), and the European Union. The EU-PREFACE project funded by the EU FP7/2007–2013 programme (Grant No. 603521) contributed to results synthesized here. LCC was supported by the UERJ/Prociencia-2018 research grant. JOS received funding from the Cluster of Excellence Future Ocean (EXC80-DFG), the EU-PREFACE project (Grant No. 603521) and the BMBF-AWA project (Grant No. 01DG12073C)